Nowcasting The Business Cycle presents a practical guide for analyzing recession risk—the primary risk factor that drives success and failure in business, finance, wealth management, and so much more. Whether you're an individual investor watching over your retirement account; the owner of a small business; a manager running a billion-dollar pension fund; or a CEO in charge of a global corporation, a large portion of triumph and defeat is closely linked with the broad swings in the economy. The business cycle, in other words, is the mother of all known (and recurring) risk factors. Accordingly, developing a process for assessing the likelihood of this threat is critical. Everyone needs a reliable, timely warning system that's relatively uncomplicated and transparent. Drawing on economic theory and macro's historical record, Nowcasting The Business Cycle outlines a simple but effective model for identifying those times when a new recession has probably started. This isn't forecasting, which is a fool's errand when it comes to the economy. Instead, the goal is recognizing when a majority of key indicators have already reached a tipping point. That may sound like a trivial advantage, but most people—including many economists—don't fully recognize when a recession has begun until the deterioration is obvious. By that point, the opportunity has probably passed for taking defensive measures in your investment portfolio, your business, or your career. The real challenge is less about predicting and more about developing solid intuition for recognizing when the macro threat is exceptionally high. Even a small degree of progress here can provide a considerable boost to your strategic insight. If we can learn the techniques for recognizing a cyclical downturn's presence relatively early—soon after it's begun, or just as it's starting—we'll have an advantage that tends to elude most folks. Nowcasting The Business Cycle provides a roadmap for ensuring that you won't be caught by surprise when the next recession strikes. That's a crucial advantage for one powerful reason: There's always another recession coming.

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. "Economics of Digitization "identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. "Economics of Digitization" will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research.

Presents the empirical data of business cycles and the theories that economists have developed to explain and prevent them, and considers case studies of recessions and depressions in the United States and internationally. Despite more than two centuries of debate, a definitive explanation of the causes of economic cycles still does not exist. Economists, politicians, and policymakers have argued many well-known theories as to why these peaks and slumps occur, and cyclical recessions and depressions continue in spite of the enormous intellectual reserves working to prevent them. This timely analysis presents a comprehensive overview of global economics, assessing older theories alongside of new ways of thinking to reveal the empirical methods needed to evaluate, forecast, and prevent future crises. Educator and economist Todd Knoop provides explanations of influential macroeconomic theories that have shaped modern economics, such as Keynesian economics, Neoclassical economics, Austrian economics, and New Keynesian economics. In addition, he considers case studies of specific recessions and depressions, beginning with the Great Depression through the East Asian crisis and Great Recession in Japan and culminating with a detailed examination of the European debt crisis and the 2008 global financial crisis. The work concludes with a look at the insights gained from these fiscal events as well as the major questions that still remain unanswered as a result of these crises.

Policymakers and business practitioners are eager to gain access to reliable information on the state of the economy for timely decision making. More so now than ever. Traditional economic indicators have been criticized for delayed reporting, out-of-date methodology, and neglecting some aspects of the economy. Recent advances in economic theory, econometrics, and information technology have fueled research in building broader, more accurate, and higher-frequency economic indicators. This volume contains contributions from a group of prominent economists who address alternative economic indicators, including indicators in the financial market, indicators for business cycles, and indicators of economic uncertainty.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.